Spectral-Spatial Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2404.18401v1
• Date: Mon, 29 Apr 2024 03:36:05 GMT
• Title: Spectral-Spatial Mamba for Hyperspectral Image Classification
• Authors: Lingbo Huang, Yushi Chen, Xin He,
• Abstract summary: spectral-spatial Mamba (SS-Mamba) is applied to hyperspectral image (HSI) classification. The proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results.
• Score: 23.215920983979426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep learning models have achieved excellent performance in hyperspectral image (HSI) classification. Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, Transformer has the problem of quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of Transformers. Therefore, in this paper, we make a preliminary attempt to apply the Mamba to HSI classification, leading to the proposed spectral-spatial Mamba (SS-Mamba). Specifically, the proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB block consists of two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, respectively. Besides, the feature enhancement module modulates spatial and spectral tokens using HSI sample's center region information. In this way, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results compared with the state-of-the-art methods. The Mamba-based method opens a new window for HSI classification.

Related papers

• Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification [27.04370747400184]
  This paper introduces the Spatial-Spectral Morphological Mamba (MorpMamba) model in which, a token generation module first converts the hyperspectral image patch into spatial-spectral tokens. These tokens are processed by morphological operations, which compute structural and shape information using depthwise separable convolutional operations. Experiments on widely used HSI datasets demonstrate that the MorpMamba model outperforms (parametric efficiency) both CNN and Transformer models.
  arXiv  Detail & Related papers  (2024-08-02T16:28:51Z)
• Empowering Snapshot Compressive Imaging: Spatial-Spectral State Space Model with Across-Scanning and Local Enhancement [51.557804095896174]
  We introduce a State Space Model with Across-Scanning and Local Enhancement, named ASLE-SSM, that employs a Spatial-Spectral SSM for global-local balanced context encoding and cross-channel interaction promoting. Experimental results illustrate ASLE-SSM's superiority over existing state-of-the-art methods, with an inference speed 2.4 times faster than Transformer-based MST and saving 0.12 (M) of parameters.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• GroupMamba: Parameter-Efficient and Accurate Group Visual State Space Model [66.35608254724566]
  State-space models (SSMs) have showcased effective performance in modeling long-range dependencies with subquadratic complexity. However, pure SSM-based models still face challenges related to stability and achieving optimal performance on computer vision tasks. Our paper addresses the challenges of scaling SSM-based models for computer vision, particularly the instability and inefficiency of large model sizes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:58Z)
• DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification [10.329381824237434]
  We propose a novel lightweight parallel design called lightweight dual-stream Mamba-convolution network (DualMamba) for HSI classification. Specifically, a parallel lightweight Mamba and CNN block are first developed to extract global and local spectral-spatial features. Compared with state-of-the-art HSI classification methods, experimental results demonstrate that DualMamba achieves significant classification accuracy.
  arXiv  Detail & Related papers  (2024-06-11T08:26:42Z)
• 3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification [14.341510793163138]
  We propose a novel 3D-Spectral-Spatial Mamba framework for HSI classification. A 3D-Spectral-Spatial Selective Scanning mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks.
  arXiv  Detail & Related papers  (2024-05-21T04:10:26Z)
• Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification [4.389334324926174]
  This study introduces the innovative Mamba-in-Mamba (MiM) architecture for HSI classification, the first attempt of deploying State Space Model (SSM) in this task. MiM model includes 1) A novel centralized Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2) A Tokenized Mamba (T-Mamba) encoder, and 3) A Weighted MCS Fusion (WMF) module. Experimental results from three public HSI datasets demonstrate that our method outperforms existing baselines and state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-05-20T13:19:02Z)
• SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising [13.1240990099267]
  We introduce a memory-efficient spatial-spectralamba (SSUMamba) for HSI denoising. Mamba is known for its remarkable long-range dependency modeling capabilities. SSUMamba achieves superior denoising results with lower memory consumption per batch compared to transformer-based methods.
  arXiv  Detail & Related papers  (2024-05-02T20:44:26Z)
• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [64.11157141177208]
  We propose a spectral enhanced rectangle Transformer to model the spatial and spectral correlation in hyperspectral images. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise.
  arXiv  Detail & Related papers  (2023-04-03T09:42:13Z)
• Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction [138.04956118993934]
  We propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST) CST embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing.
  arXiv  Detail & Related papers  (2022-03-09T16:17:47Z)
• Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks [51.15952040322895]
  One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. We propose several approaches to HSI segmentation based on M-GSP feature extraction. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.
  arXiv  Detail & Related papers  (2021-11-29T23:28:18Z)
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [127.20208645280438]
  Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T16:59:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.